计算机科学
生成语法
生成对抗网络
人工智能
对抗制
深度学习
机器学习
鉴别器
生成模型
图像(数学)
模式识别(心理学)
作者
Jiabin Liu,Hanyuan Hang,Bo Wang,Biao Li,Huadong Wang,Yingjie Tian,Yong Shi
出处
期刊:IEEE Transactions on Systems, Man, and Cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2021-07-16
卷期号:: 1-12
标识
DOI:10.1109/tcyb.2021.3089337
摘要
Learning from complementary labels (CLs) is a useful learning paradigm, where the CL specifies the classes that the instance does not belong to, instead of providing the ground truth as in the ordinary supervised learning scenario. In general, although it is less laborious and more efficient to collect CLs compared with ordinary labels, the less informative signal in the complementary supervision is less helpful to learn competent feature representation. Consequently, the final classifier's performance greatly deteriorates. In this article, we leverage generative adversarial networks (GANs) to derive an algorithm GAN-CL to effectively learn from CLs. In addition to the role in original GAN, the discriminator also serves as a normal classifier in GAN-CL, with the objective constructed partly with the complementary information. To further prove the effectiveness of our schema, we study the global optimality of both generator and discriminator for the GAN-CL under mild assumptions. We conduct extensive experiments on benchmark image datasets using deep models, to demonstrate the compelling improvements, compared with state-of-the-art CL learning approaches.
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